prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Plots about particles in ExaTrkX routine.
For plot data requirement, detail list below:
- hits:
- required: hit_id, x, y, z or r, phi, z
- pairs:
- required: hit_id_1, hit_id_2
- edges:
- required: hit_id_1, hit_id_2,
- opt... | pd.Series(['r', 'phi']) | pandas.Series |
import pandas as pd
from business_rules.operators import (DataframeType, StringType,
NumericType, BooleanType, SelectType,
SelectMultipleType, GenericType)
from . import TestCase
from decimal import Decimal
import sys
import pandas
class Str... | pandas.Series([True, True, True]) | pandas.Series |
import os
import pandas as pd
import xfeat
from xfeat import ArithmeticCombinations, ConcatCombination, CountEncoder, LabelEncoder
from ayniy.preprocessing import xfeat_runner, xfeat_target_encoding
from ayniy.utils import FeatureStore
categorical_cols = [
"Type",
"Breed1",
"Breed2",
"Gender",
"C... | pd.read_csv("../input/petfinder-adoption-prediction/train/train.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
import re
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_bool_dtype, is_categorical, is_categorical_dtype,
is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype,
is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_interval_dtype,
... | PeriodDtype('2D') | pandas.core.dtypes.dtypes.PeriodDtype |
import pandas as pd
import numpy as np
import scipy
import os, sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pylab
import matplotlib as mpl
import seaborn as sns
import analysis_utils
from multiprocessing import Pool
sys.path.append('../utils/')
from game_utils import *
in_d... | pd.io.parsers.read_csv(synthetic_dir + '/' + game) | pandas.io.parsers.read_csv |
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from pprint ... | pd.DataFrame(results['params']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import pearsonr
# from mpl_toolkits.axes_grid1 import host_subplot
# import mpl_toolkits.axisartist as AA
# import matplotlib
import matplotlib.pyplot as plt
import matplotlib.t... | pd.Grouper(freq="M") | pandas.Grouper |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | date_range('1/1/2012', freq='23Min', periods=384) | pandas.date_range |
# -*- coding: utf-8 -*-
# *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# ... | pd.Series(series_data) | pandas.Series |
import numpy as np
import pandas as pd
import pytest
from rayml.objectives import SensitivityLowAlert
from rayml.tests.objective_tests.test_binary_classification_objective import (
TestBinaryObjective,
)
class TestSLA(TestBinaryObjective):
__test__ = True
def assign_objective(self, alert_rate):
... | pd.Series([True, True, False, False]) | pandas.Series |
import codecs
import datetime
import functools
import json
import os
import re
import shutil
import pandas as pd
from dateutil.relativedelta import relativedelta
from requests.exceptions import ConnectionError
from utils_pandas import add_data
from utils_pandas import cut_ages
from utils_pandas import export
from uti... | pd.to_datetime(df['Notified date'], dayfirst=True, errors="coerce") | pandas.to_datetime |
"""
This code translates .mdl files and produces
- csv file: detailed descriptives of all variables
- doc file: file with information used later in the testing battery
- equi file: creates file for user input for equilibrium test
- py file: translated .mdl file using pysd
- model stats: model statis... | pd.DataFrame(model_vars) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import re
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_bool_dtype, is_categorical, is_categorical_dtype,
is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype,
is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_interval_dtype,
... | Categorical([1, 2], categories=[1, 2, 3], ordered=True) | pandas.Categorical |
from copy import deepcopy
import numpy as np
import pandas as pd
import pytest
from Bio import Alphabet
from Bio.Seq import reverse_complement, Seq
from Bio.SeqRecord import SeqRecord
from pandas.util.testing import assert_series_equal, assert_index_equal
from sklearn.pipeline import Pipeline
from crseek import estimat... | pd.Series(loci, index=index) | pandas.Series |
""" This module provides the functionality to calculate ephemeris for two bodies problem
also in the case of perturbed methods. More advance pertubed methods will be handled
in other module
"""
# Standard library imports
import logging
from math import isclose
from typing import ForwardRef
# Third party imports
imp... | pd.DataFrame(result) | pandas.DataFrame |
#! /usr/bin/env python3
import argparse
import re,sys,os,math,gc
import numpy as np
import pandas as pd
import matplotlib as mpl
import copy
import math
from math import pi
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
f... | pd.read_table(lists,sep='\t',names=['sample','chrs','matrix']) | pandas.read_table |
# coding: utf-8
# NIDEM LiDAR tidal tagging
#
# This script imports multiple xyz .csv files for each LiDAR validation site, converts GPS timestamps to UTC, then
# uses these to compute tide heights at the exact moment each point was acquired during the LiDAR survey.
# Non-inundated points are then identified by select... | pd.concat(df_list) | pandas.concat |
from __future__ import absolute_import, division, unicode_literals
import unittest
import jsonpickle
from helper import SkippableTest
try:
import pandas as pd
import numpy as np
from pandas.testing import assert_series_equal
from pandas.testing import assert_frame_equal
from pandas.testing import... | pd.period_range(start='2017-01-01', end='2018-01-01', freq='M') | pandas.period_range |
## TECHNICHAL ANALYSIS
import pandas as pd
import numpy as np
# import talib
from plotly.graph_objs import Figure
from .utils import make_list
class StudyError(Exception):
pass
def _ohlc_dict(df_or_figure,open='',high='',low='',close='',volume='',
validate='',**kwargs):
"""
Returns a dictionary with the act... | pd.concat([df,__df],axis=1) | pandas.concat |
import os
import fnmatch
import calendar
import numpy as np
import pandas as pd
import xarray as xr
from itertools import product
from util import month_num_to_string
import xesmf as xe
"""
Module contains several functions for preprocessing S2S hindcasts.
Author: <NAME>, NCAR (<EMAIL>)
Contributions from <NAME>, N... | pd.date_range(start=start_range, end=end_range, freq='D') | pandas.date_range |
import pathlib
import datetime
import time
import uuid
import pandas as pd
import numpy as np
import simpy
import dill as pickle
import openclsim.model
def save_logs(simulation, location, file_prefix):
# todo add code to LogSaver to allow adding a file_prefix to each file
site_logs = list(simulation.site... | pd.concat([unique_df, object_df], ignore_index=True) | pandas.concat |
import numpy as np
import pandas as pd
import datetime as dt
import pickle
import bz2
from .analyzer import summarize_returns
DATA_PATH = '../backtest/'
class Portfolio():
"""
Portfolio is the core class for event-driven backtesting. It conducts the
backtesting in the following order:
1. Initializati... | pd.Series() | pandas.Series |
"""Tests for climTrend.
Author: <NAME>
"""
from climvis import climtrend
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import bokeh
def test_get_lat_lon():
city = 'Innsbruck'
city_2 = climtrend.cities_list[1]
lat_corr = 47.2666667
lon_corr = 11.4
lat, lon = climtrend.ge... | pdt.assert_frame_equal(df_resample_winter, df_corr_winter) | pandas.util.testing.assert_frame_equal |
"""
Additional tests for PandasArray that aren't covered by
the interface tests.
"""
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.arrays import PandasArray
from pandas.core.arrays.numpy_ import PandasDtype
@pytest.fixture(
params=[
np.array(["a", "b"], dty... | pd.Series([1, 2, 3]) | pandas.Series |
import json
import numpy as np
import random, csv, math
from collections import OrderedDict
from queue import PriorityQueue
import argparse, os
import time
from textwrap import wrap
import subprocess
import os, sys
import libs.inputs as inputs
import shutil
import random
from shutil import copyfile
import libs.query_j... | pd.DataFrame(data_list) | pandas.DataFrame |
import eikon as ek # the Eikon Python wrapper package
import numpy as np # NumPy
import pandas as pd # pandas
import configparser as cp
import warnings
import sys
# underlying use case
warnings.filterwarnings("ignore")
df = pd.read_csv(r'C:\Users\segul\OneDrive\Documents\ReutersTickers.csv', header=None)
... | pd.merge(data, result, left_on='Primary CDS RIC', right_on='Instrument', how='left') | pandas.merge |
"""
The TypedDict class
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# ... | _pandas.Series(lst, dtype=object) | pandas.Series |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-05-14 00:00:00") | pandas.Timestamp |
import os
import tempfile
import unittest
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tests.settings import POSTGRESQL_ENGINE, SQLITE_ENGINE
from tests.utils import get_repository_path, DBTest
from ukbrest.common.pheno2sql import Pheno2SQL
class Pheno2SQLTest(DBTest):
@unitt... | pd.isnull(chunk.loc[4, 'c21_2_0']) | pandas.isnull |
from ast import parse
from operator import indexOf
from typing import OrderedDict
import numpy as np
from numpy.lib.function_base import rot90
from pandas.io.parsers import read_csv
import torch.utils.data as data_utils
import pandas as pd
import matplotlib.pyplot as plt
from torch.utils.data.dataset import Su... | pd.DataFrame(data, columns=['y_Predicted', 'y_Actual']) | pandas.DataFrame |
import statsmodels.api as sm
from statsmodels.sandbox.nonparametric import kernels
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
'''特征空间解析
我们将特征类型分为如下四种
- numeric：连续的特征，表现为可以定义序关系且唯一值可以有无数个
- category：类别型特征
- Multi-category：多类别
- object：无结构数据，暂不提供任何解析
'''
d... | pd.Series(y) | pandas.Series |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.encode(i, orient="values") | pandas._libs.json.encode |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | tm.assert_series_equal(result, a[a]) | pandas._testing.assert_series_equal |
import logging, os, sys, pickle, json, time, yaml
from datetime import datetime as dt
import warnings
warnings.filterwarnings('ignore')
from tqdm import tqdm
tqdm.pandas()
import pandas as pd
import geopandas as gpd
from geopandas.plotting import _plot_linestring_collection, _plot_point_collection
import numpy as np
... | pd.merge(df_raw_oilwells, iso2[['country','iso2']], how='left',left_on='md_country',right_on='country') | pandas.merge |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Description"""
import logging
import flask
import numpy as np
import pandas as pd
logger = logging.getLogger(__name__)
serve_app = flask.Flask(__name__)
@serve_app.route("/ping", methods=["GET"])
def ping():
return flask.Response(response="\n", status=status, mim... | pd.read_csv(s, header=None) | pandas.read_csv |
import pandas as pd
import seaborn as sns
from etherscan import Etherscan
import streamlit as st
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
def without_hue( plot, feature, title, criteria, x_axis_rotation=0, _format=None):
sns.set(rc={'figure.figsize':(11.7,8.27)})
plot.... | pd.read_csv(path_transac_history, index_col=[0]) | pandas.read_csv |
# from warnings import warn
# from faps.alogsumexp import alogsumexp
from operator import pos
import numpy as np
import pandas as pd
import faps as fp
from glob import glob
from tqdm import tqdm
import os
def import_mcmc(folder, burnin):
"""
Import files with MCMC output for A. majus mating parameters
Glo... | pd.DataFrame(summarise) | pandas.DataFrame |
import pandas as pd
import numpy as np
from prettytable import PrettyTable
def delete_na(dataframes, dtypes):
'''
Objective:
- Delete all NA's from the dataframes passed
Input:
- dataframes : String of the tables and their selected columns
- dtypes : Numerical types
Output:
... | pd.get_dummies(table[variable]) | pandas.get_dummies |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import librosa
import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.preprocessing.text import Tokenizer
from keras.preprocessin... | pd.read_pickle(EMOTION_LABEL_PICKLE) | pandas.read_pickle |
"""
The BIGMACC script.
"""
import os
import pandas as pd
import numpy as np
import logging
import xarray as xr
import zarr
from itertools import repeat
import time
import cea.utilities.parallel
logging.getLogger('numba').setLevel(logging.WARNING)
import cea.config
import cea.utilities
import cea.inputlocator
import c... | pd.DataFrame.from_dict(data[1]) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from constants import *
from datetime import datetime
import lightgbm as lgb
import numpy as np
from sklearn.model_selection import KFold
import pandas as pd
import utils
import os
import ndcg_tools
import math
import gc
import sys
seed = SEED
cur_stage = CU... | pd.Series(user2stage) | pandas.Series |
import string
import pandas as pd
import numpy as np
import doctest
from texthero import preprocessing, stopwords
from . import PandasTestCase
"""
Test doctest
"""
def load_tests(loader, tests, ignore):
tests.addTests(doctest.DocTestSuite(preprocessing))
return tests
class TestPreprocessing(PandasTestCa... | pd.Series("https://tests.com \n https://tests.com") | pandas.Series |
# -*- coding: UTF-8 -*-
"""
Created by louis at 2021/9/13
Description:
"""
import os
import gc
import glob
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import pandas as pd
import time
from itertools import islice
from torch.utils.data import Dataset, ... | pd.DataFrame(full_seconds_in_bucket) | pandas.DataFrame |
# pylint: disable=redefined-outer-name,protected-access
# pylint: disable=missing-function-docstring,missing-module-docstring,missing-class-docstring
"""This module contains tests of the tabulator Data Grid"""
# http://tabulator.info/docs/4.7/quickstart
# https://github.com/paulhodel/jexcel
import pandas as pd
impor... | pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": ["a", "b", "c", "d", "e"]}) | pandas.DataFrame |
import os
# Reduce CPU load. Need to perform BEFORE import numpy and some other libraries.
os.environ['MKL_NUM_THREADS'] = '2'
os.environ['OMP_NUM_THREADS'] = '2'
os.environ['NUMEXPR_NUM_THREADS'] = '2'
import gc
import math
import copy
import json
import numpy as np
import pandas as pd
import torch as th
import torch... | pd.DataFrame({'word': words, 'embedding': embeddings}) | pandas.DataFrame |
"""
Short summary.
Extract the features of the ego-noise data...
"""
import os
import shutil
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import librosa
import matplotlib.pyplot as plt
from aircraft_detector.utils.utils import (
retrieve_files,
get_feature_direc... | pd.read_csv(file_states, header=0) | pandas.read_csv |
# encode=utf-8
"""
一维数组
"""
import numpy as np
ndarry = np.array([[35, 20, 66], [23, 67, 89], [13, 244, 67]], np.int32)
print(ndarry.shape, ndarry.size)
print(ndarry.dtype)
print(ndarry[1:2, 1:2])
import pandas as pd
stocks = pd.Series([20.1, 100.0, 66.5], index=['tx', 'tobao', 'apple'])
stocks2 = | pd.Series([23.1, 95, 88], index=['tx', 'tobao', 'google']) | pandas.Series |
#!/usr/bin/env python3
import glob
import os
import pprint
import traceback
import pandas as pd
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
# Extraction function
def tflog2pandas(path: str) -> pd.DataFrame:
"""convert single tensorflow log file to pandas DataFrame
Par... | pd.concat([runlog_data, r]) | pandas.concat |
import scipy.io.wavfile as wav
from python_speech_features import mfcc
import numpy as np
import os
import pandas as pd
CLASSICAL_DIR = "C:\\Users\\<NAME>\\Music\\Classical\\"
METAL_DIR = "C:\\Users\\<NAME>\\Music\\Metal\\"
JAZZ_DIR = "C:\\Users\\<NAME>\\Music\\Jazz\\"
POP_DIR = "C:\\Users\\<NAME>\\Music\\Pop\\"
PATH... | pd.DataFrame(cov) | pandas.DataFrame |
import os
import sys
import datetime
import numpy as np
import scipy.signal
import pandas as pd
import yfinance as yf
from contextlib import contextmanager
from src.utils_date import add_days
from src.utils_date import prev_weekday
#from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
ERROR_NO_MINUTE_DATA_YT... | pd.to_datetime(df['datetime']) | pandas.to_datetime |
import pandas as pd
import pytest
import woodwork as ww
from woodwork.logical_types import Boolean, Double, Integer
from rayml.exceptions import MethodPropertyNotFoundError
from rayml.pipelines.components import (
ComponentBase,
FeatureSelector,
RFClassifierSelectFromModel,
RFRegressorSelectFromModel,
... | pd.Series([1, 2, 1]) | pandas.Series |
# pylint:disable=missing-docstring,redefined-outer-name
import pytest
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
from survey_toolkit.core import MultipleChoiceQuestion
@pytest.fixture
def question():
return MultipleChoiceQuestion('favouritePhones', 'What are your favour... | pd.Series(answers, name='favouritePhones') | pandas.Series |
# import libraries that we need
import glob, os, re
import pandas as pd
from lib.export import export_files
from lib.filesearch import find_participants, find_highest_export
# import custom-made functions that we'll need
from lib.sorting import Sorting, process_surfaces, merge_all_dataframes, extract_survey
# set roo... | pd.read_csv(gazesurface_path) | pandas.read_csv |
from itertools import combinations
import numpy as np
import pandas as pd
import pytest
from synthesized_insight.check import ColumnCheck
from synthesized_insight.metrics import (
CramersV,
DistanceCNCorrelation,
DistanceNNCorrelation,
EarthMoversDistance,
EarthMoversDistanceBinned,
HellingerD... | pd.Series([1, 2, 3, 1, 2, 3, 1, 2, 3] * 100, name='a') | pandas.Series |
import json
import os
import pickle
import boto3
import numpy as np
import pandas as pd
from ticket_closure_lib.transformers import DateColTransformer # noqa
from ticket_closure_lib.transformers import FeatureRemover # noqa
from ticket_closure_lib.transformers import OrdinalConverter # noqa
class TicketPredictor:... | pd.to_datetime(in_df[date_col], format="%d/%m/%Y %H:%M") | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Combined scheduling and planning models (deterministic and robust).
'''
import pyomo.environ as pyomo
from pyomo.opt import SolverStatus, TerminationCondition
import numpy as np
import time
import pandas as pd
import dill
import collections
from stn.deg import degradat... | pd.DataFrame(columns=units) | pandas.DataFrame |
from unittest.mock import patch
import pytest
from AWS_AACT_Pipeline.categorize_driver import Driver
from AWS_AACT_Pipeline.mock_db_manager import MockDatabaseManager
from AWS_AACT_Pipeline.categorizer import Categorizer
from AWS_AACT_Pipeline.mock_db import MockDatabase
import pandas as pd
def test_missing_json_fil... | pd.DataFrame(columns=['nct_id', 'color_category'], index=['kylie', 'willy', 'riley', 'ben', 'jonah']) | pandas.DataFrame |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | MultiIndex(levels=[['a'], ['b']], labels=[[0, 0, 0, 0], [0, 0]]) | pandas.core.index.MultiIndex |
#!/usr/bin/env python
'''
<NAME> October 2018
Scripts for looking at and evaluating input data files for dvmdostem.
Generally data has been prepared by M. Lindgren of SNAP for the IEM project and
consists of directories of well labled .tif images, with one image for each
timestep.
This script has (or will have) a var... | pd.DatetimeIndex(start=hdf.index[0], end=pncar_df.index[-1], freq="MS") | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 17:45:11 2018
@author: <NAME>
@e-mail: <EMAIL>
Program for analysis and creation of fragmentation diagrams in mass spectrometry out of .csv files
"""
import os
import time
from tkinter import filedialog
import pandas as pd
import numpy as np
from numpy import tra... | pd.DataFrame(data[1]) | pandas.DataFrame |
# coding: utf-8
from collections import OrderedDict
import pandas as pd
from czsc.objects import Signal, Factor, Event, Freq, Operate, PositionLong, PositionShort
def test_signal():
s = Signal(k1="1分钟", k3="倒1形态", v1="类一买", v2="七笔", v3="基础型", score=3)
assert str(s) == "Signal('1分钟_任意_倒1形态_类一买_七笔_基础型_3')"
... | pd.to_datetime('2021-01-03') | pandas.to_datetime |
from visions.core.model import VisionsBaseType, VisionsTypeset
from visions.core.implementations.types import visions_generic
from visions.core.model.relations import IdentityRelation
import pandas.api.types as pdt
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
class visions_statistical_set(Vi... | pdt.is_numeric_dtype(series) | pandas.api.types.is_numeric_dtype |
# --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#Code starts here
data = pd.read_csv(path)
data['Rating'].hist()
data = data[data['Rating']<=5]
data['Rating'].hist()
#Code ends here
# --------------
# code starts here
total_null = data.isnull().... | pd.to_datetime(data['Last Updated']) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.metrics import mean_sq... | pd.read_csv(path + 'bases_lidia/anos_iniciais/ideb_escola_2007_ai.csv') | pandas.read_csv |
import os
import sys
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, compat
from pandas.util import testing as tm
class TestToCSV:
@pytest.mark.xfail((3, 6, 5) > sys.version_info >= (3, 5),
reason=("Python csv library bug "
... | pd.MultiIndex.from_arrays([['foo'], ['bar']]) | pandas.MultiIndex.from_arrays |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
#
# This file contains functions used to analyse data sets for a single
# test case.
import sys
import argparse
import glob
import os
import pandas as pd # Data manipulation and analysis
import datetime as dt
from StatisticsFunctions import StatisticsFunctions as sf
import p... | pd.DataFrame() | pandas.DataFrame |
import pyaniasetools as aat
import pyanitools as ant
import hdnntools as hdt
import pandas as pd
import sys
import numpy as np
import re
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import LogNorm
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1.inset_locator imp... | pd.set_option('expand_frame_repr', False) | pandas.set_option |
import numpy as np
import pandas as pd
from datetime import datetime
from functools import partial
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer, Input, Dense, Dropout, BatchNormalization
from tensorflow.keras import metrics
from sklearn import preprocessing... | pd.Series(test_preds, index=self.X_test.index) | pandas.Series |
import unittest
import pandas as pd
import numpy as np
class TestNumpyJSONEncoder(unittest.TestCase):
def setUp(self):
from bokeh.protocol import NumpyJSONEncoder
self.encoder = NumpyJSONEncoder()
def test_fail(self):
self.assertRaises(TypeError, self.encoder.default, {'testing': 1})... | pd.Series([1, 3, 5, 6, 8]) | pandas.Series |
import pandas as pd
import re
default_units = {'speed': 'km/h',
'distance': 'km',
'weight': 'kg',
'height': 'cm'}
units_conversions = {}
# Team 2
def convert_time(time: str, time_format: str = None, mode: str = 'flag'):
"""
Converts string with time int... | pd.to_datetime(date, 'ignore', format=date_format) | pandas.to_datetime |
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib.font_manager import FontProperties
import os, sys, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
grandpadir = os.path.dirname(os.path.dirname(currentdir))
sys.path.insert(0, grandpadir)
from ... | pd.concat([best_models_perf_in_sample, best_models_perf_in_sample_curr], axis=1) | pandas.concat |
import pandas
import glob
daily_report_files = glob.glob('data/daily_reports/*.csv')
all_data = pandas.DataFrame({'Kommune': [],
'Last Update Day': [],
'Last Update Time': [],
'Confirmed': [],
'Deaths': [],... | pandas.Timestamp(day) | pandas.Timestamp |
# --------------
# Importing header files
import numpy as np
import pandas as pd
from scipy.stats import mode
import warnings
warnings.filterwarnings('ignore')
#Reading file
bank_data = pd.read_csv(path)
bank = | pd.DataFrame(bank_data) | pandas.DataFrame |
import pandas as pd
import dataset
import albumentations as A
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
from albumentations.pytorch.transforms import ToTensorV2
from tqdm import tqdm
from albumentations import (
HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, Rand... | pd.DataFrame(columns=['patientId', 'x', 'y', 'width', 'height']) | pandas.DataFrame |
#!/usr/bin/env python
import os
import sys
import datetime
from pathlib import Path
from functools import partial
import numpy as np
import pandas as pd
from tqdm import tqdm
from scipy import optimize
from tqdm.contrib import concurrent
from lib.io import read_file
from lib.utils import ROOT
def _get_outbreak_mas... | pd.Series(projected, index=date_indices, name="Estimated") | pandas.Series |
from itertools import product as it_product
from typing import List, Dict
import numpy as np
import os
import pandas as pd
from scipy.stats import spearmanr, wilcoxon
from provided_code.constants_class import ModelParameters
from provided_code.data_loader import DataLoader
from provided_code.dose_evaluation_class imp... | pd.read_csv(consolidate_data_paths['ref_dvh'], index_col=[0, 1, 2, 3], squeeze=True) | pandas.read_csv |
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pyarrow as pa
import pytest
from pandas.arrays import SparseArray
from kartothek.core.cube.constants import (
KTK_CUBE_DF_SERIALIZER,
KTK_CUBE_METADATA_DIMENSION_COLUMNS,
KTK_CUBE_METADATA_KEY_IS_SEED,
KTK_CUBE_METADATA_PARTITIO... | pd.DataFrame({"x": [2, 3], "p": [1, 1], "v": [12, 13]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat May 9 19:30:38 2020
@author: aletu
"""
import numpy as np
import pandas as pd
import random
import datetime
def generateWarehouseData(num_SKUs: int = 100,
nodecode: int = 1,
idwh: list = ['LOGICAL_WH1', 'LOGICAL_WH2', 'FA... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 30 15:15:03 2016
@author: Manuel
"""
from C45Tree_own import branchingCriterion
from C45Tree_own import split
import pandas as pa
def fit(X,y, branching = "gainRatio", splitCriterion = "infoGain", splitNumeric = "binary", gain_thres = 0):
'''This function fits a... | pa.DataFrame() | pandas.DataFrame |
#
#
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import *
import math
class VolatilityArbitrage(object):
def __init__(self):
self.refl = ''
def startup(self):
print('VolatilityArbitrage v0.0.3')
self.ds_file = './data/50ETF.xlsx'
... | pd.DataFrame() | pandas.DataFrame |
import json
from datetime import datetime
import pandas as pd
from autogluon import TabularPrediction as task
data_path = "./data/plasma/plasma"
label_column = "RETPLASMA"
fold1 = | pd.read_csv(data_path + "-fold1.csv") | pandas.read_csv |
import requests
import json
import urllib
import pandas as pd
from vikuatools.utils import int_to_string, remove_value_from_dict_key, parse_properties
def hs_get_recent_modified(url, parameters, max_results):
"""
Get recent modified object from hubspot API legacy
url: str endpoint to retreive. one of deals... | pd.DataFrame(list_properties) | pandas.DataFrame |
# _*_ encoding:utf-8 _*_
# This script calculates index market capture by day through coingekco api
# market capture = index market cap / sum(each composition's market cap in the index )
# prerequisite:
# 1. install coingecko api python library https://github.com/man-c/pycoingecko
# 2. prepare index compositi... | pd.read_csv(index_info_dir) | pandas.read_csv |
import json
import pandas as pd
from scipy.stats.stats import pearsonr, spearmanr
import numpy as np
from scipy import stats
import sys
import matplotlib.pyplot as plt
import os
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
import argparse
def parse_args(args):
p... | pd.read_json(metric_info['path']) | pandas.read_json |
from __future__ import division
import math
import sys
from random import randint
from random import random as rnd
from reoccuring_drift_stream import ReoccuringDriftStream
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.spatial.distance import cd... | pd.DataFrame(self.w_) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 6 22:15:42 2018
@author: katezeng
This module is for Predictive Analysis - Hypothesis Testing
- This component contains both the traditional statistical hypothesis testing, and the beginning of machine learning predictive analytics.
Her... | pd.factorize(data['price_group']) | pandas.factorize |
# LIBRARIES
# set up backend for ssh -x11 figures
import matplotlib
matplotlib.use('Agg')
# read and write
import os
import sys
import glob
import re
import fnmatch
import csv
import shutil
from datetime import datetime
# maths
import numpy as np
import pandas as pd
import math
import random
# miscellaneous
import ... | pd.DataFrame({'version': versions, 'R2': r2s}) | pandas.DataFrame |
import pytest
import numpy as np
import pandas
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
import matplotlib
import modin.pandas as pd
from modin.pandas.utils import to_pandas
from numpy.testing import assert_array_equal
from .utils import (
random_state,
RAND_LOW,
RAND_... | pandas.DataFrame(data, index=["date", "value"]) | pandas.DataFrame |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 31 19:06:02 2018
@author: Jessica
"""
from __future__ import division, print_function
from sklearn.datasets import fetch_mldata
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.metrics import ... | pd.DataFrame(x_time) | pandas.DataFrame |
from constants_and_util import *
from scipy.stats import norm, pearsonr, spearmanr
import pandas as pd
import copy
import numpy as np
import random
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from scipy.stats impo... | pd.DataFrame(results_df) | pandas.DataFrame |
from sklearn.metrics.pairwise import euclidean_distances
from human_ISH_config import *
import pandas as pd
import numpy as np
import scipy
from sklearn import metrics
import json
import os
#print (pd.show_versions())
def create_diagonal_mask(low_to_high_map, target_value=1):
"""
Create a block diagonal mask... | pd.concat(train_val_eval_df_list, sort=False) | pandas.concat |
# code stolen from https://github.com/xhochy/nyc-taxi-fare-prediction-deployment-example
# download the data from https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
# use the pandas code snippet from here
# https://github.com/xhochy/nyc-taxi-fare-prediction-deployment-example/blob/main/training/Train.ipynb
#... | pd.read_parquet("data/yellow_tripdata_2016-01.parquet", columns=used_columns) | pandas.read_parquet |
import os
import pandas as pd
import numpy as np
def read_config(filename):
"""
Read and parse configuration file containing stored user variables.
These variables are then passed to the analysis notebooks
and input to pipeline functions.
"""
f = open(filename)
config_dict = {}
for li... | pd.read_csv(metadata_file, header=0, sep="\t", index_col=0) | pandas.read_csv |
import csv
import numpy as np
from matplotlib import pyplot as plt
import scipy.stats as stats
import pandas as pd
def read_data(datafile):
data = pd.read_csv(datafile)
return np.array(data['x']), np.array(data['y'])
def plotdata(data, color):
for x in data:
plt.plot(x[0], x[1], color)
def plotGaussian(mu, var... | pd.DataFrame(pandas, columns=['Baudrate', 'mu_x', 'var_x', 'sigma_x', 'mu_y', 'var_y', 'sigma_y']) | pandas.DataFrame |
# Copyright 2017 QuantRocket LLC - All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | pd.concat(breakdown_parts, axis=1, sort=True) | pandas.concat |
import numpy as np
import pandas as pd
from glob import glob
import matplotlib.pyplot as plt
'''
turbine-05_helihoist-1_tom_acc-vel-pos_hammerhead_2019-09-10-16-04-47_2019-09-20-02-53-43
turbine-05_helihoist-1_tom_geometry_hammerhead_2019-09-10-16-04-47_2019-09-20-02-53-43
turbine-05_helihoist-1_tom_acc-vel-pos_sbi1_... | pd.read_csv('environment/environment/waves/wmb-sued/wmb-sued_2019-09-18.csv', delimiter = ' ') | pandas.read_csv |
import logging
import os
import numpy as np
import pandas as pd
import sqlalchemy
from cached_property import cached_property
from scipy.interpolate import interp1d
from aqueduct.errors import Error
class RiskService(object):
def __init__(self, user_selections):
# DB Connection
self.engine = sql... | pd.DataFrame(index=[self.geogunit_name]) | pandas.DataFrame |
from __future__ import print_function
# this is a class to deal with aqs data
from builtins import zip
from builtins import range
from builtins import object
import os
from datetime import datetime
from zipfile import ZipFile
import pandas as pd
from numpy import array, arange
import inspect
import requests
class AQ... | pd.to_numeric(df.State_Code, errors='coerce') | pandas.to_numeric |
#!/usr/bin/python
# coding=utf-8
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import jieba
import jieba.analyse
import os
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.charts import Pie
from pyecharts.charts import Bar
from... | pd.read_csv(csv_path) | pandas.read_csv |
'''
example of loading FinMind api
'''
from FinMind.Data import Load
import requests
import pandas as pd
url = 'http://finmindapi.servebeer.com/api/data'
list_url = 'http://finmindapi.servebeer.com/api/datalist'
translate_url = 'http://finmindapi.servebeer.com/api/translation'
'''----------------TaiwanStockInfo-----... | pd.DataFrame(temp['data']) | pandas.DataFrame |
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